The plot line:This is an article that was part of a special issue of Forest Science that was born from a conference on the importance of carbon in deep forest soils (it is not surprising that, yes, soil scientists think it is important!). This article emphasizes the need for understanding the pattern of carbon content as one goes deeper into forest soils. The pattern is highly relevant because deep soils are rarely sampled because of the physical difficulty involved in getting to deep soils (it’s a lot of digging). If one knows the pattern pretty well, then one can sample shallow soils and then estimate how much carbon is deeper if they are confident of the pattern. They found two basic shapes- linear (total carbon increases at a constant rate with depth) and asymptotic (total carbon increases at lower rates as you go deeper). The linear soils tended to have about 50% of their carbon below 20 cm (the common depth of sampling), while asymptotic tended to have about 35% below 20cm. The conclusions seemed to be that, it is reasonable to sample only part of the soil horizon and then extrapolate for estimating lower depths, but the correct extrapolation equation (i.e. the mathematical representation of the pattern) has to be used.

This article is largely for academics, so it is difficult to find direct relevance for landowners and stakeholders. Instead, I refer readers to the previous discussion of carbon accounting on September 4, 2009. The main point of that discussion- that we are a long ways off from being confident in estimating carbon in forests with great accuracy- still seems to be the case today. Studies like this, however, move us closer to the “gold standard” in carbon accounting that will someday be needed for true carbon markets to develop.

Relevance to managers:

I recently tried to bone up on the issue of carbon in forest soils because foresters are now required to address impacts of forest treatments upon carbon when conducting environmental impact reports. I liked this article because it focused on missing data- specifically data estimating the carbon in the deep soil profiles that are usually not sampled. For managers, the entire amount of carbon in soils is often virtually unknown compared to above ground carbon. It has always been part of a foresters job to understand how much wood volume (i.e. carbon) is standing above-ground in the forest, but it has only recently become part of our job to also understand how much carbon is below-ground.

One item of relevance from this article seems to be that there are two major sources of variability when it comes to carbon in soils: one is the pattern at which carbon content changes as one gets deeper in soils; the other is the total amount of carbon that exists from location to location. The latter can be estimated, but only if the former is understood with relatively good certainty. When applying estimates of belowground carbon to total forest carbon budgets, it probably makes sense to check to see how deep soil carbon is estimated, if at all.

Another relevant point is that there is likely to be carbon in very deep horizons that is unaccounted for when below ground carbon is estimated. In this article, soil carbon was estimated down to between ½ and 1 meter. Where deeper soils exist, a significant amount of carbon is not being estimated if carbon amounts are extrapolated to only ½ or 1 meter. This is less of a problem for soils with asymptotic patterns of soil carbon (the amount of carbon declines with depth). But even for these soils, a true asymptote was not reached within ½ to 1 meter depth so carbon would still remain unaccounted.

Critique (I always have one, no matter how good the article is):

My only critique is that the selection of the two models used to represent the pattern of how carbon changes with soil were not justified in a statistical sense. They fit patterns to either a logarithmic or asymptotic (a “Langmuir” equation) model, but don’t explain how one or the other model was selected. Often it is the correlation coefficient or a model selection criteria that is used to pick the “best” model. It is only a minor critique since both models are relatively parsimonious and do not have as much need for a model selection approach.